Overview

Dataset statistics

Number of variables21
Number of observations41188
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)< 0.1%
Total size in memory6.6 MiB
Average record size in memory168.0 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

Dataset has 12 (< 0.1%) duplicate rowsDuplicates
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rateHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rate and 2 other fieldsHigh correlation
euribor3m is highly correlated with emp.var.rate and 2 other fieldsHigh correlation
nr.employed is highly correlated with previous and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rateHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
loan is highly correlated with housingHigh correlation
housing is highly correlated with loanHigh correlation
contact is highly correlated with monthHigh correlation
month is highly correlated with contactHigh correlation
age is highly correlated with jobHigh correlation
job is highly correlated with age and 1 other fieldsHigh correlation
education is highly correlated with jobHigh correlation
housing is highly correlated with loanHigh correlation
loan is highly correlated with housingHigh correlation
contact is highly correlated with month and 5 other fieldsHigh correlation
month is highly correlated with contact and 5 other fieldsHigh correlation
pdays is highly correlated with previous and 4 other fieldsHigh correlation
previous is highly correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly correlated with pdays and 6 other fieldsHigh correlation
emp.var.rate is highly correlated with contact and 6 other fieldsHigh correlation
cons.price.idx is highly correlated with contact and 6 other fieldsHigh correlation
cons.conf.idx is highly correlated with contact and 8 other fieldsHigh correlation
euribor3m is highly correlated with contact and 9 other fieldsHigh correlation
nr.employed is highly correlated with contact and 8 other fieldsHigh correlation
y is highly correlated with cons.conf.idx and 2 other fieldsHigh correlation
previous has 35563 (86.3%) zeros Zeros

Reproduction

Analysis started2022-10-10 15:52:52.624846
Analysis finished2022-10-10 15:53:32.881300
Duration40.26 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02406041
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:33.246504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42124998
Coefficient of variation (CV)0.2603746315
Kurtosis0.7913115312
Mean40.02406041
Median Absolute Deviation (MAD)7
Skewness0.7846968158
Sum1648511
Variance108.6024512
MonotonicityNot monotonic
2022-10-10T21:23:33.446909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311947
 
4.7%
321846
 
4.5%
331833
 
4.5%
361780
 
4.3%
351759
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391432
 
3.5%
Other values (68)24204
58.8%
ValueCountFrequency (%)
175
 
< 0.1%
1828
 
0.1%
1942
 
0.1%
2065
 
0.2%
21102
 
0.2%
22137
 
0.3%
23226
 
0.5%
24463
1.1%
25598
1.5%
26698
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8822
0.1%
871
 
< 0.1%
868
 
< 0.1%
8515
< 0.1%

job
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
admin.
10422 
blue-collar
9254 
technician
6743 
services
3969 
management
2924 
Other values (7)
7876 

Length

Max length13
Median length12
Mean length8.955229679
Min length6

Characters and Unicode

Total characters368848
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin.10422
25.3%
blue-collar9254
22.5%
technician6743
16.4%
services3969
 
9.6%
management2924
 
7.1%
retired1720
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Length

2022-10-10T21:23:33.679621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin10422
25.3%
blue-collar9254
22.5%
technician6743
16.4%
services3969
 
9.6%
management2924
 
7.1%
retired1720
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%
Other values (2)1205
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e47273
12.8%
n35547
 
9.6%
a33327
 
9.0%
l31618
 
8.6%
i30657
 
8.3%
c26709
 
7.2%
r21031
 
5.7%
m19765
 
5.4%
d16512
 
4.5%
t14593
 
4.0%
Other values (14)91816
24.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter347751
94.3%
Dash Punctuation10675
 
2.9%
Other Punctuation10422
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47273
13.6%
n35547
10.2%
a33327
9.6%
l31618
9.1%
i30657
8.8%
c26709
 
7.7%
r21031
 
6.0%
m19765
 
5.7%
d16512
 
4.7%
t14593
 
4.2%
Other values (12)70719
20.3%
Dash Punctuation
ValueCountFrequency (%)
-10675
100.0%
Other Punctuation
ValueCountFrequency (%)
.10422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin347751
94.3%
Common21097
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47273
13.6%
n35547
10.2%
a33327
9.6%
l31618
9.1%
i30657
8.8%
c26709
 
7.7%
r21031
 
6.0%
m19765
 
5.7%
d16512
 
4.7%
t14593
 
4.2%
Other values (12)70719
20.3%
Common
ValueCountFrequency (%)
-10675
50.6%
.10422
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII368848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47273
12.8%
n35547
 
9.6%
a33327
 
9.0%
l31618
 
8.6%
i30657
 
8.3%
c26709
 
7.2%
r21031
 
5.7%
m19765
 
5.4%
d16512
 
4.5%
t14593
 
4.0%
Other values (14)91816
24.9%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
married
24928 
single
11568 
divorced
4612 
unknown
 
80

Length

Max length8
Median length7
Mean length6.831115859
Min length6

Characters and Unicode

Total characters281360
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married24928
60.5%
single11568
28.1%
divorced4612
 
11.2%
unknown80
 
0.2%

Length

2022-10-10T21:23:33.879634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:34.120182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
married24928
60.5%
single11568
28.1%
divorced4612
 
11.2%
unknown80
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.1%
m24928
8.9%
a24928
8.9%
n11808
 
4.2%
s11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (6)14156
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter281360
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.1%
m24928
8.9%
a24928
8.9%
n11808
 
4.2%
s11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (6)14156
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin281360
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.1%
m24928
8.9%
a24928
8.9%
n11808
 
4.2%
s11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (6)14156
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII281360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.1%
m24928
8.9%
a24928
8.9%
n11808
 
4.2%
s11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (6)14156
 
5.0%

education
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
university.degree
12168 
high.school
9515 
basic.9y
6045 
professional.course
5243 
basic.4y
4176 
Other values (3)
4041 

Length

Max length19
Median length17
Mean length12.7109595
Min length7

Characters and Unicode

Total characters523539
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree12168
29.5%
high.school9515
23.1%
basic.9y6045
14.7%
professional.course5243
12.7%
basic.4y4176
 
10.1%
basic.6y2292
 
5.6%
unknown1731
 
4.2%
illiterate18
 
< 0.1%

Length

2022-10-10T21:23:34.488505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:34.705120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree12168
29.5%
high.school9515
23.1%
basic.9y6045
14.7%
professional.course5243
12.7%
basic.4y4176
 
10.1%
basic.6y2292
 
5.6%
unknown1731
 
4.2%
illiterate18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e59194
 
11.3%
i51643
 
9.9%
s49925
 
9.5%
.39439
 
7.5%
o36490
 
7.0%
r34840
 
6.7%
h28545
 
5.5%
c27271
 
5.2%
y24681
 
4.7%
n22604
 
4.3%
Other values (15)148907
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter471587
90.1%
Other Punctuation39439
 
7.5%
Decimal Number12513
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59194
12.6%
i51643
11.0%
s49925
10.6%
o36490
 
7.7%
r34840
 
7.4%
h28545
 
6.1%
c27271
 
5.8%
y24681
 
5.2%
n22604
 
4.8%
g21683
 
4.6%
Other values (11)114711
24.3%
Decimal Number
ValueCountFrequency (%)
96045
48.3%
44176
33.4%
62292
 
18.3%
Other Punctuation
ValueCountFrequency (%)
.39439
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin471587
90.1%
Common51952
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59194
12.6%
i51643
11.0%
s49925
10.6%
o36490
 
7.7%
r34840
 
7.4%
h28545
 
6.1%
c27271
 
5.8%
y24681
 
5.2%
n22604
 
4.8%
g21683
 
4.6%
Other values (11)114711
24.3%
Common
ValueCountFrequency (%)
.39439
75.9%
96045
 
11.6%
44176
 
8.0%
62292
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII523539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59194
 
11.3%
i51643
 
9.9%
s49925
 
9.5%
.39439
 
7.5%
o36490
 
7.0%
r34840
 
6.7%
h28545
 
5.5%
c27271
 
5.2%
y24681
 
4.7%
n22604
 
4.3%
Other values (15)148907
28.4%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
no
32588 
unknown
8597 
yes
 
3

Length

Max length7
Median length2
Mean length3.043702049
Min length2

Characters and Unicode

Total characters125364
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowunknown
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no32588
79.1%
unknown8597
 
20.9%
yes3
 
< 0.1%

Length

2022-10-10T21:23:34.945117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:35.105119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no32588
79.1%
unknown8597
 
20.9%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n58379
46.6%
o41185
32.9%
u8597
 
6.9%
k8597
 
6.9%
w8597
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter125364
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n58379
46.6%
o41185
32.9%
u8597
 
6.9%
k8597
 
6.9%
w8597
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin125364
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n58379
46.6%
o41185
32.9%
u8597
 
6.9%
k8597
 
6.9%
w8597
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII125364
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n58379
46.6%
o41185
32.9%
u8597
 
6.9%
k8597
 
6.9%
w8597
 
6.9%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

housing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
yes
21576 
no
18622 
unknown
 
990

Length

Max length7
Median length3
Mean length2.644022531
Min length2

Characters and Unicode

Total characters108902
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
yes21576
52.4%
no18622
45.2%
unknown990
 
2.4%

Length

2022-10-10T21:23:35.305317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:35.473120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
yes21576
52.4%
no18622
45.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n21592
19.8%
y21576
19.8%
e21576
19.8%
s21576
19.8%
o19612
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter108902
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n21592
19.8%
y21576
19.8%
e21576
19.8%
s21576
19.8%
o19612
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin108902
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n21592
19.8%
y21576
19.8%
e21576
19.8%
s21576
19.8%
o19612
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII108902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n21592
19.8%
y21576
19.8%
e21576
19.8%
s21576
19.8%
o19612
18.0%
u990
 
0.9%
k990
 
0.9%
w990
 
0.9%

loan
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
no
33950 
yes
6248 
unknown
 
990

Length

Max length7
Median length2
Mean length2.271875303
Min length2

Characters and Unicode

Total characters93574
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
no33950
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Length

2022-10-10T21:23:35.641124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:35.809323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
no33950
82.4%
yes6248
 
15.2%
unknown990
 
2.4%

Most occurring characters

ValueCountFrequency (%)
n36920
39.5%
o34940
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter93574
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n36920
39.5%
o34940
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin93574
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n36920
39.5%
o34940
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII93574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n36920
39.5%
o34940
37.3%
y6248
 
6.7%
e6248
 
6.7%
s6248
 
6.7%
u990
 
1.1%
k990
 
1.1%
w990
 
1.1%

contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
cellular
26144 
telephone
15044 

Length

Max length9
Median length8
Mean length8.365252015
Min length8

Characters and Unicode

Total characters344548
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular26144
63.5%
telephone15044
36.5%

Length

2022-10-10T21:23:35.969123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:36.153314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular26144
63.5%
telephone15044
36.5%

Most occurring characters

ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter344548
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin344548
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII344548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
may
13769 
jul
7174 
aug
6178 
jun
5318 
nov
4101 
Other values (5)
4648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may13769
33.4%
jul7174
17.4%
aug6178
15.0%
jun5318
 
12.9%
nov4101
 
10.0%
apr2632
 
6.4%
oct718
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Length

2022-10-10T21:23:36.297121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:36.505118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
may13769
33.4%
jul7174
17.4%
aug6178
15.0%
jun5318
 
12.9%
nov4101
 
10.0%
apr2632
 
6.4%
oct718
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123564
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
thu
8623 
mon
8514 
wed
8134 
tue
8090 
fri
7827 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu8623
20.9%
mon8514
20.7%
wed8134
19.7%
tue8090
19.6%
fri7827
19.0%

Length

2022-10-10T21:23:36.729104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:36.953119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
thu8623
20.9%
mon8514
20.7%
wed8134
19.7%
tue8090
19.6%
fri7827
19.0%

Most occurring characters

ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123564
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

duration
Real number (ℝ≥0)

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.2850102
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:37.153114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile752.65
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.2792488
Coefficient of variation (CV)1.003849386
Kurtosis20.24793801
Mean258.2850102
Median Absolute Deviation (MAD)94
Skewness3.263141255
Sum10638243
Variance67225.72888
MonotonicityNot monotonic
2022-10-10T21:23:37.345115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90170
 
0.4%
85170
 
0.4%
136168
 
0.4%
73167
 
0.4%
124164
 
0.4%
87162
 
0.4%
72161
 
0.4%
104161
 
0.4%
111160
 
0.4%
106159
 
0.4%
Other values (1534)39546
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
412
 
< 0.1%
530
 
0.1%
637
0.1%
754
0.1%
869
0.2%
977
0.2%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
35091
< 0.1%
34221
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%

campaign
Real number (ℝ≥0)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567592503
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:37.537121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770013543
Coefficient of variation (CV)1.078836903
Kurtosis36.97979514
Mean2.567592503
Median Absolute Deviation (MAD)1
Skewness4.762506697
Sum105754
Variance7.672975028
MonotonicityNot monotonic
2022-10-10T21:23:37.713023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
334
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.475454
Minimum0
Maximum999
Zeros15
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:37.889330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.9109073
Coefficient of variation (CV)0.194198103
Kurtosis22.22946263
Mean962.475454
Median Absolute Deviation (MAD)0
Skewness-4.922189916
Sum39642439
Variance34935.68728
MonotonicityNot monotonic
2022-10-10T21:23:38.049946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
99939673
96.3%
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
Other values (17)205
 
0.5%
ValueCountFrequency (%)
015
 
< 0.1%
126
 
0.1%
261
 
0.1%
3439
1.1%
4118
 
0.3%
546
 
0.1%
6412
1.0%
760
 
0.1%
818
 
< 0.1%
964
 
0.2%
ValueCountFrequency (%)
99939673
96.3%
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
193
 
< 0.1%
187
 
< 0.1%
178
 
< 0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1729629989
Minimum0
Maximum7
Zeros35563
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:38.201751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949010798
Coefficient of variation (CV)2.861311858
Kurtosis20.10881622
Mean0.1729629989
Median Absolute Deviation (MAD)0
Skewness3.832042243
Sum7124
Variance0.2449270788
MonotonicityNot monotonic
2022-10-10T21:23:38.362169image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
 
0.5%
2754
 
1.8%
14561
 
11.1%
035563
86.3%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size321.9 KiB
nonexistent
35563 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.45372439
Min length7

Characters and Unicode

Total characters430568
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent35563
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Length

2022-10-10T21:23:38.553745image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-10T21:23:38.922410image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent35563
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430568
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin430568
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII430568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08188550063
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17191
Negative (%)41.7%
Memory size321.9 KiB
2022-10-10T21:23:39.066995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.570959741
Coefficient of variation (CV)19.18483405
Kurtosis-1.062631525
Mean0.08188550063
Median Absolute Deviation (MAD)0.3
Skewness-0.7240955492
Sum3372.7
Variance2.467914506
MonotonicityNot monotonic
2022-10-10T21:23:39.195191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.416234
39.4%
-1.89184
22.3%
1.17763
18.8%
-0.13683
 
8.9%
-2.91663
 
4.0%
-3.41071
 
2.6%
-1.7773
 
1.9%
-1.1635
 
1.5%
-3172
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.41071
 
2.6%
-3172
 
0.4%
-2.91663
 
4.0%
-1.89184
22.3%
-1.7773
 
1.9%
-1.1635
 
1.5%
-0.210
 
< 0.1%
-0.13683
 
8.9%
1.17763
18.8%
1.416234
39.4%
ValueCountFrequency (%)
1.416234
39.4%
1.17763
18.8%
-0.13683
 
8.9%
-0.210
 
< 0.1%
-1.1635
 
1.5%
-1.7773
 
1.9%
-1.89184
22.3%
-2.91663
 
4.0%
-3172
 
0.4%
-3.41071
 
2.6%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57566437
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:39.347678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.578840049
Coefficient of variation (CV)0.00618579684
Kurtosis-0.8298085772
Mean93.57566437
Median Absolute Deviation (MAD)0.38
Skewness-0.2308876514
Sum3854194.464
Variance0.3350558023
MonotonicityNot monotonic
2022-10-10T21:23:39.499677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947763
18.8%
93.9186685
16.2%
92.8935794
14.1%
93.4445175
12.6%
94.4654374
10.6%
93.23616
8.8%
93.0752458
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
92.201770
 
1.9%
92.379267
 
0.6%
92.431447
 
1.1%
92.469178
 
0.4%
92.649357
 
0.9%
92.713172
 
0.4%
92.75610
 
< 0.1%
92.843282
 
0.7%
92.8935794
14.1%
92.963715
 
1.7%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%
94.055229
 
0.6%
94.027233
 
0.6%
93.9947763
18.8%
93.9186685
16.2%
93.876212
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50260027
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41188
Negative (%)100.0%
Memory size321.9 KiB
2022-10-10T21:23:39.667674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.628197856
Coefficient of variation (CV)-0.1142691537
Kurtosis-0.3585583105
Mean-40.50260027
Median Absolute Deviation (MAD)4.4
Skewness0.3031798587
Sum-1668221.1
Variance21.4202154
MonotonicityNot monotonic
2022-10-10T21:23:39.819676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47763
18.8%
-42.76685
16.2%
-46.25794
14.1%
-36.15175
12.6%
-41.84374
10.6%
-423616
8.8%
-47.12458
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12458
 
6.0%
-46.25794
14.1%
-45.910
 
< 0.1%
-42.76685
16.2%
-423616
8.8%
-41.84374
10.6%
-40.8715
 
1.7%
ValueCountFrequency (%)
-26.9447
 
1.1%
-29.8267
 
0.6%
-30.1357
 
0.9%
-31.4770
 
1.9%
-33172
 
0.4%
-33.6178
 
0.4%
-34.6174
 
0.4%
-34.8264
 
0.6%
-36.15175
12.6%
-36.47763
18.8%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621290813
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:40.059674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734447405
Coefficient of variation (CV)0.4789583313
Kurtosis-1.406802622
Mean3.621290813
Median Absolute Deviation (MAD)0.108
Skewness-0.7091879564
Sum149153.726
Variance3.0083078
MonotonicityNot monotonic
2022-10-10T21:23:40.275676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572868
 
7.0%
4.9622613
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651071
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24636
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
0.63916
 
< 0.1%
0.6410
 
< 0.1%
0.64235
0.1%
0.64323
0.1%
0.64438
0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968992
 
2.4%
4.967643
 
1.6%
4.966622
 
1.5%
4.9651071
2.6%
4.9641175
2.9%
4.9632487
6.0%
4.9622613
6.3%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.035911
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2022-10-10T21:23:40.436710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.25152767
Coefficient of variation (CV)0.01398316732
Kurtosis-0.003760375696
Mean5167.035911
Median Absolute Deviation (MAD)37.1
Skewness-1.044262407
Sum212819875.1
Variance5220.28325
MonotonicityNot monotonic
2022-10-10T21:23:40.589771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.116234
39.4%
5099.18534
20.7%
51917763
18.8%
5195.83683
 
8.9%
5076.21663
 
4.0%
5017.51071
 
2.6%
4991.6773
 
1.9%
5008.7650
 
1.6%
4963.6635
 
1.5%
5023.5172
 
0.4%
ValueCountFrequency (%)
4963.6635
 
1.5%
4991.6773
 
1.9%
5008.7650
 
1.6%
5017.51071
 
2.6%
5023.5172
 
0.4%
5076.21663
 
4.0%
5099.18534
20.7%
5176.310
 
< 0.1%
51917763
18.8%
5195.83683
8.9%
ValueCountFrequency (%)
5228.116234
39.4%
5195.83683
 
8.9%
51917763
18.8%
5176.310
 
< 0.1%
5099.18534
20.7%
5076.21663
 
4.0%
5023.5172
 
0.4%
5017.51071
 
2.6%
5008.7650
 
1.6%
4991.6773
 
1.9%

y
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
False
36548 
True
4640 
ValueCountFrequency (%)
False36548
88.7%
True4640
 
11.3%
2022-10-10T21:23:40.757697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Interactions

2022-10-10T21:23:29.112117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:11.436518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:13.520586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:15.513808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:17.627041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:19.515472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:21.404546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:23.429296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:25.294785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:27.079072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:29.303923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:11.797130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:13.728621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:15.705824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:17.835028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:19.755386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:21.588529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:23.629882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:25.478822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:27.271038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:29.488103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:11.981070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:13.912589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:15.905833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:18.043021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:19.939674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:21.780565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:23.806353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:25.646823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:27.471039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:29.688109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:12.168237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:14.113162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:16.090049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:18.267267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:20.123430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:21.948534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:23.990542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:25.854778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:27.823181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:29.856387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:12.352238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:14.329396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:16.290900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:18.443534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:20.307684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:22.324966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:24.174518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:26.030789image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:28.031539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:30.040419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:12.552234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:14.537440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:16.515218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:18.627518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:20.499698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:22.508975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:24.398727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:26.206783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:28.199516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:30.224412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:12.752306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:14.713507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:16.867224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:18.796412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:20.670563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:22.692967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:24.566719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:26.374780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:28.375512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:30.400414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:12.952265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:14.953814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:17.051308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:18.995501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:20.852006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:22.909312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:24.750612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:26.569122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:28.551830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:30.648414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:13.127959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:15.129836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:17.235216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:19.163478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:21.028093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:23.085319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:24.942821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:26.750963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:28.720129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:30.816414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:13.336363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:15.313836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:17.427023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:19.339493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:21.233468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:23.253321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:25.110783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:26.910794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-10T21:23:28.896135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-10T21:23:40.909696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-10T21:23:41.197704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-10T21:23:41.526014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-10T21:23:41.822074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-10T21:23:42.102560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-10T21:23:31.168260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-10T21:23:31.952699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
056housemaidmarriedbasic.4ynononotelephonemaymon26119990nonexistent1.193.994-36.44.8575191.0no
157servicesmarriedhigh.schoolunknownnonotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.0no
237servicesmarriedhigh.schoolnoyesnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.0no
340admin.marriedbasic.6ynononotelephonemaymon15119990nonexistent1.193.994-36.44.8575191.0no
456servicesmarriedhigh.schoolnonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
545servicesmarriedbasic.9yunknownnonotelephonemaymon19819990nonexistent1.193.994-36.44.8575191.0no
659admin.marriedprofessional.coursenononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
741blue-collarmarriedunknownunknownnonotelephonemaymon21719990nonexistent1.193.994-36.44.8575191.0no
824techniciansingleprofessional.coursenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
925servicessinglehigh.schoolnoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no

Last rows

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
4117862retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
4118036admin.marrieduniversity.degreenononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
4118137admin.marrieduniversity.degreenoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
4118229unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
4118446blue-collarmarriedprofessional.coursenononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
4118644technicianmarriedprofessional.coursenononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri23939991failure-1.194.767-50.81.0284963.6no

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy# duplicates
024servicessinglehigh.schoolnoyesnocellularaprtue11419990nonexistent-1.893.075-47.11.4235099.1no2
127techniciansingleprofessional.coursenononocellularjulmon33129990nonexistent1.493.918-42.74.9625228.1no2
232techniciansingleprofessional.coursenoyesnocellularjulthu12819990nonexistent1.493.918-42.74.9685228.1no2
335admin.marrieduniversity.degreenoyesnocellularmayfri34849990nonexistent-1.892.893-46.21.3135099.1no2
436retiredmarriedunknownnononotelephonejulthu8819990nonexistent1.493.918-42.74.9665228.1no2
539admin.marrieduniversity.degreenononocellularnovtue12329990nonexistent-0.193.200-42.04.1535195.8no2
639blue-collarmarriedbasic.6ynononotelephonemaythu12419990nonexistent1.193.994-36.44.8555191.0no2
741technicianmarriedprofessional.coursenoyesnocellularaugtue12719990nonexistent1.493.444-36.14.9665228.1no2
845admin.marrieduniversity.degreenononocellularjulthu25219990nonexistent-2.992.469-33.61.0725076.2yes2
947techniciandivorcedhigh.schoolnoyesnocellularjulthu4339990nonexistent1.493.918-42.74.9625228.1no2